Key Technical Requirements and Architectural Directions for Cloud Databases

Large enterprises, especially in finance and banking, are moving toward micro‑service architectures that demand native cloud capabilities; consequently, databases must evolve into cloud‑native platforms (dbPaaS) that provide elasticity, multi‑tenancy, and self‑management on top of IaaS infrastructure.

Technical requirements of cloud databases include elastic scaling of capacity, flexible deployment of database functions, continuous data reliability and availability, compute‑storage separation, multi‑model storage (structured, semi‑structured, unstructured, graph), self‑management (zero‑downtime maintenance, CI, rolling upgrades), self‑monitoring and automated fault repair, plug‑in components for specific scenarios, and compliance‑driven security.

Architectural directions focus on three main trends: (1) storage‑SQL separation, where the storage engine, SQL processing layer, and metadata are loosely coupled, enabling independent scaling and plug‑in storage engines; (2) multi‑model architectures that allow a single platform to serve relational, JSON, graph, and object storage workloads, reducing operational complexity; and (3) disaster‑recovery and active‑active designs that provide transparent data replication, cross‑region consistency, and automatic failover without application changes.

The storage‑SQL separation model is exemplified by modern MySQL and Aurora implementations and by SequoiaDB 3.0, which uses MySQL’s parser for full compatibility while delegating storage to a distributed engine that offers elastic expansion.

Multi‑model support enables unified management of diverse data types, catering to use cases ranging from traditional transaction processing to historical data services, real‑time online analytics, and large‑scale image storage, each with distinct performance and consistency needs.

Active‑active and disaster‑recovery capabilities ensure that applications experience seamless data access even during regional outages, with automatic data synchronization and topology adjustments that maintain high availability and compliance with regulatory requirements.

Advantages of cloud database architectures include eliminating the need for manual sharding, flexible scaling of compute and storage, compatibility with existing applications, multi‑engine support, enhanced data security through replication, and the ability to meet stringent regulatory standards.

Application scenarios covered are traditional transaction services requiring high throughput and ACID guarantees, historical data services needing long‑term storage and HTAP capabilities, real‑time online services demanding low latency, and image storage services that prioritize reliability, scalability, and multi‑model handling.

In summary, cloud databases represent a critical evolution in data management; future developments will continue to emphasize multi‑model engines, compute‑storage separation, and advanced elasticity to meet the growing demands of cloud‑native enterprises.